Key result
A multi-iteration moving average filter approach for denoising ECG signals provided the greatest signal-to-noise ratio improvement and reduced distortion metrics by 50% or more.
Effect estimate: reduction of 50% or more
A novel multi-iteration moving average filter approach effectively denoises ECG signals, improving signal-to-noise ratio and reducing distortion by at least 50%.
The noise within an electrocardiogram signal can cause errors that are viewed in the results of different ECG characteristics, in both amplitude and time interval which ultimately lead to a incorrect diagnosis of cardiac disease. In this paper, a new approach of de-noising the electrocardiogram signal is proposed using multi-iteration of the moving average filter. The algorithm of the proposed approach includes two main steps: first to estimate the amount of noise presents in the ECG signal, second to remove the noise added. The proposed de-noising approach is validated with ECG records which were collected from the MIT-BIH ECG database with different amounts of additive gauss white noise. The validation results prove the robustness of proposed de-noising approach to provide the greatest signal to noise ratio improvement, and to give a reduction of 50% or more in terms of standard metrics used for computing distortion in a noisy signal. Additionally, the filtered signal has a smooth shape in comparison with the adopted de-noising ECG signal techniques.
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Salih et al. (2015) studied Cardiac disease (ECG signal denoising). Multi-iteration moving average filter vs. Adopted de-noising ECG signal techniques was evaluated on Signal to noise ratio improvement and reduction in distortion metrics (reduction of 50% or more). A multi-iteration moving average filter approach for denoising ECG signals provided the greatest signal-to-noise ratio improvement and reduced distortion metrics by 50% or more.
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